作者
Yen-Ting Lee, Tao Ban, Tzu-Ling Wan, Shin-Ming Cheng, Ryoichi Isawa, Takeshi Takahashi, Daisuke Inoue
发表日期
2020/12/29
研讨会论文
2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
页码范围
775-784
出版商
IEEE
简介
In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous malware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and …
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YT Lee, T Ban, TL Wan, SM Cheng, R Isawa… - 2020 IEEE 19th International Conference on Trust …, 2020